7 citations · 29 across the 18 of their papers we have counts for
4 papers · 1 filter
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees
Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh +1
We revisit the domain of off-policy policy optimization in RL from the perspective of coordinate ascent. One commonly-used approach is to leverage the off-policy policy gradient to…
Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots
Wei Hung, Bo-Kai Huang, Ping-Chun Hsieh +1
Many real-world continuous control problems are in the dilemma of weighing the pros and cons, multi-objective reinforcement learning (MORL) serves as a generic framework of learnin…
Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265
Yung-Han Ho, Chia-Hao Kao, Wen-Hsiao Peng +1
This paper presents a reinforcement learning (RL) framework that utilizes Frank-Wolfe policy optimization to solve Coding-Tree-Unit (CTU) bit allocation for Region-of-Interest (ROI…
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits
Yu-Heng Hung, Ping-Chun Hsieh
Reward-biased maximum likelihood estimation (RBMLE) is a classic principle in the adaptive control literature for tackling explore-exploit trade-offs. This paper studies the stocha…